Children and Political Violence: At the Intersection of Rights and Realities
Bibliographic record
Abstract
At the Intersection of Rights and RealitiesFollowing a decline in the 1990s, armed conflicts have almost tripled since 2000 (von Einsiedel et al., 2014), becoming more frequent, more complex and affecting more people (Overseas Development Institute (ODI), 2016).At the same time, militant armed groups, such as Al-Shabaab, Boko Haram and Islamic State, have become powerful facilitators of violence, displaying significant control over territory and people.In 2014, there were 40 recorded armed conflictsdefined by one or more states contributing troops to one or both warring sides (Pettersson and Wallensteen, 2015).The number of places experiencing political violence is even higher, with political violence broadly defined as violence outside state control perpetrated to achieve political goals (e.g.revolution, civil war and terrorism) (O'Neil, 2012).At the time of writing, the places experiencing the worst humanitarian emergencies as a result of political violence include Syria, Democratic Republic of the Congo, Iraq, South Sudan, Afghanistan, Yemen, the Lake Chad basin and Libya, while other conflicts, such as in Turkey and Burundi, are poised to deteriorate (Gu ehenno, 2016).The increase in armed conflict and political violence represents the highest level of human suffering since World War II.In May 2016, as a response to the growing number of displaced persons around the world, the United Nations convened the first-ever World Humanitarian Summit in Istanbul.According to the UN Secretary General's report for the Summit (Ki-Moon, 2016), as a consequence of political violence, there are currently 125 million people in need of humanitarian assistance, and 60 million people who have been forced to flee their homes and/or countries as internally displaced persons or refugees.Research continues to demonstrate that it is children who will experience the harshest and the most permanent effects of political violence (Apfel and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".